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Published: August 26, 2008

 
 

Web Sales with a Human Touch

Bringing personalized service to e-commerce consumers.

The late Ben Feldman, known as the greatest life insurance salesman of all time, once said, “Selling is 98 percent understanding human beings and 2 percent product knowledge.” Call it Feldman’s Law. That view has never gotten much traction in the world of e-commerce, where the mantra has been to minimize human contact with customers. To be sure, many e-tailers endeavor to gather as much knowledge as possible about customer behavior and buying habits by aggregating and crunching massive amounts of data on users’ online buying habits. But those are just dry numbers and statistics. The plain truth is that even the most successful, tech-savvy retail Web sites still convert only 1 to 3 percent of visitors into buyers, largely because Web-based salesmanship is such a blunt instrument.

Suppose, however, that you could migrate Feldman’s Law to the Web, using the very technological virtues that make e-commerce so potent a sales channel, and bring in the human touch at exactly the moment it would be most effective. How much would that be worth? According to 24/7 Customer, a business process outsourcing firm based in Campbell, Calif., with clients as varied as Adobe Systems Inc. and Capital One Services Inc., the human touch used in this way can increase online consumer conversion rates by 15 percent or more. To prove this, 24/7 has developed predictive software called SalesNext that sorts online visitors into hot and cold leads and then makes personalized contact through online chat with the most promising prospects to close the deal. “It’s as if you could translate the judgment and timing of a top salesperson at Brooks Brothers or Best Buy straight onto the Web,” says 24/7 CEO and cofounder P.V. Kannan.

The flow of consumers from the category of mere visitors to that of actual buyers, in any sales channel, is like liquid passing through a funnel. At a real-world retail outlet, the marketing portion of the funnel at the top is poorly targeted because companies have limited control over who visits a store. The power of the funnel lies at the bottom, where seasoned salespeople convert store visitors into buyers. However, the top part of the typical e-commerce funnel is potentially very efficient. Advanced Web marketing techniques can target prospects entering the online retail site on the basis of prior Web behavior and other historical data and drive them to items that match their past preferences. But the bottom part of the funnel narrows to a trickle, because most Web sites’ one-size-fits-all consumer experience — which at best may include a chat feature that relies on wooden scripts with little variation for different customer types — makes conversion of those visitors into buyers much more difficult. However, by separating the tire kickers from the hot leads, then chatting with those leads in a way that personalizes their experience and drives them toward a transaction, Web retailers can open up the bottom of the funnel significantly.

Plenty of retail Web sites offer live human-to-human chat with consumers; what distinguishes 24/7 Customer’s approach is its ability to offer chat only to those who might not otherwise buy. Getting to the stage at which a visitor is invited to chat involves a series of filters de­signed to predict which individuals are most likely to buy as a result of a chat, rather than through self- service. After all, there’s no point in needlessly cannibalizing the lower-cost automated channel. As a visitor browses the Web site, she is evalu­ated on a variety of criteria, including how she was referred to the site, whether she’s visited or bought anything there before, the time of day, the day of the week, her geographical location, and the product category. Equally important is the path a consumer takes through the Web site. If she heads immediately to the spec sheet for a particular digital camera, it’s unlikely that chatting with her will influence her buying decision. But if she appears to be wavering among three different models, a chat just might help her make up her mind.

The goal at this stage is to match likely consumers with likely product choices. The program’s “rules engine” — the heuristics it follows to identify the most potentially valuable consumers — knows, for instance, that visitors browsing expensive jewelry are more likely to buy if they come from Beverly Hills than if they come from a less-affluent area. Just as important, however, is the ability of the program to learn from past transactions. Over time, as Michigan’s economic fortunes have eroded, for example, the scoring model might note that people from tony Bir­mingham, outside Detroit, are now more hesitant than they once were to buy pricey items, especially compared to, say, individuals from less-hard-hit suburban Boston.

Once a visitor is identified as a hot lead, another filter determines whether to invite him to chat — that is, the program analyzes whether talking to him is virtually the only way to convince him to make a purchase. Think of a floor clerk in a Sears major appliance department sizing up several customers and approaching the one who appears most certain to buy, using intuition drawn from experience. On one level, deciding who to invite for a chat is a simple scheduling problem: Are there enough agents available to handle the chat? Increasing the number of agents means increasing the number of invitations to chat, which in turn means approaching colder leads who are less likely to end up making a purchase. The colder the lead, the lower the potential profitability. On a more strategic level, the software must determine the number of agents that will maximize profitability. Further statistical modeling is needed to select the right agent for each consumer, depending on such criteria as the best-performing agent for the product category that individual is looking at. Even a great used-car salesman isn’t likely to make much money working at Tiffany.

Now, it’s time to chat. Here the goal is simple — to translate the art of selling into a science. Once that Sears clerk approaches a prospect, he has to use his experience to make dozens of instantaneous judgments, based on any number of visual and linguistic cues: Is the customer detail-oriented, or does he prefer a softer touch? Am I pushing too hard, and is he beginning to resist? The customer appears to be losing interest — is now the time to begin offering discounts? The 24/7 chat format, of course, does not allow for all the nuances any decent salesperson picks up in a face-to-face conversation. It does, however, perform analyses of thousands of chat transcripts, through text mining and data mining, to perfect the techniques that human customer service representatives use to close the sale.

Text mining, for example, can offer insight into how a salesperson should talk to consumers to achieve the greatest degree of success. Some of these insights are based on extensive research in neurolinguistics, which argues that people can be classified as aural, visual, or kinesthetic, depending on how they perceive the world. That classification, in turn, can provide hints of the most effective communication strategies for convincing them to buy an item. Aural consumers listen for product details, so an effective sales approach might be, “Let me tell you how many megapixels this camera has.” Visual consumers want information about the product’s appearance. Thus, the salesperson might say, “This camera comes in three exciting colors and will fit into your shirt pocket.” And kinesthetic consumers respond to pitches that tap emotions, such as, “You’ll love how this camera balances in your hand, and it’s perfect for taking pictures of your granddaughter’s nursery school graduation.”

Adobe, the software giant, rolled out SalesNext in July 2007 with excellent results. Since then, the company has seen a 15 percent jump in conversion among consum­ers who chat, says Dawn Monet, senior manager of Adobe’s worldwide call centers. And, she notes, the satisfaction of consumers who use chat is higher than that of both consumers who shop online without chat and those who shop by phone. “SalesNext really enables the ‘magic moment,’ when we can be there with the customer when and where they have a question. The customer doesn’t have to search for answers or wait in a call queue,” says Monet. “This is the beginning of how we will communicate with customers in the future. It combines the human element with the technology in a new, powerful way.”

A well-run sales chat program driven by predictive mathematical models can greatly boost a Web site’s profits and consumer loyalty, while reducing costly returns. The solution, however, doesn’t work right out of the box. Like that crack salesperson on the floor at Sears, who learns something from every encounter with a consumer, chat can get smarter day by day. The true power of sales chat, when coupled with predictive and text-mining technologies, lies in the ability to learn what works and what doesn’t, and to constantly refine the system’s filtering and selling techniques.

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Edward H. Baker, former editor of CIO Insight magazine, is a contributing editor at strategy+business.
 
 
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